A Study of Fuzzy Clustering to Archetypal Analysis

Gonçalo Mendes, Susana Nascimento

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


This paper presents a comparative study between a method for fuzzy clustering which retrieves pure individual types from data, the fuzzy clustering with proportional membership (FCPM), and an archetypal analysis algorithm based on Furthest-Sum approach (FS-AA). A simulation study comprising 82 data sets is conducted with a proper data generator, FCPM-DG, whose goal is twofold: first, to analyse the ability of archetypal clustering algorithm to recover Archetypes from data of distinct dimensionality; second, to analyse robustness of FCPM and FS-AA algorithms to outliers. The effectiveness of these algorithms are yet compared on clustering 12 diverse benchmark data sets from machine learning. The evaluation conducted with five primer unsupervised validation indices shows the good quality of the clustering solutions.
Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings
EditorsDavid Camacho, Paulo Novais, Antonio J. Tallón-Ballesteros, Hujun Yin
Number of pages12
ISBN (Print)9783030034955
Publication statusPublished - 21 Nov 2018
Event19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018 - University Autónoma de Madrid, Madrid, Spain
Duration: 21 Nov 201823 Nov 2018

Publication series

Name Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018
Abbreviated title IDEAL 2018


  • archetypal analysis
  • fuzzy clustering
  • synthetic multidimensional data
  • fuzzy validation indices

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